Developing Causal AI applications - DataScienceCentral.com

Most machine learning models are concerned with correlation. In contrast, Causal models are concerned with cause and effect relationships – for example – "How much would a power failure cost to a given manufacturing plant?" A structural causal model (SCM) represents causal dependencies using graphical models. Bayesian Networks are one of the most widely used SCMs. Bayesian Network consists of a DAG(Directed Acyclic Graph), a causal graph where nodes represent random variables and edges represent the relationship between them, and a conditional probability distribution (CPDs) associated with each of the random variables. Models can reflect both statistically significant information (learned from the data) and domain expertise simultaneously.